code: def selection(
      x_bounds,
      x_types,
      clusteringmodel_gmm_good,
      clusteringmodel_gmm_bad,
      minimize_starting_points,
      minimize_constraints_fun=None,
  ):
      """
      Select the lowest mu value
      """
      results = lib_acquisition_function.next_hyperparameter_lowest_mu(
          _ratio_scores,
          [clusteringmodel_gmm_good, clusteringmodel_gmm_bad],
          x_bounds,
          x_types,
          minimize_starting_points,
          minimize_constraints_fun=minimize_constraints_fun,
      )
  
      return results
  
file_path: nni/nni/algorithms/hpo/metis_tuner/Regression_GMM/Selection.py
imports: import os
  import random
  import sys
  from ..None import lib_acquisition_function
  from ..None import lib_constraint_summation
label_desc: Use image node_id 3 for calling a global function with example usage: selection(x_bounds, x_types, clusteringmodel_gmm_good, clusteringmodel_gmm_bad, minimize_starting_points, minimize_constraints_fun) and returns: results